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# Earth Observation

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arXiv Nov 8, 2026
Earth embeddings stop being a tangle of mixed dimensions: sparse autoencoders pull out nameable geographic features, though a random dictionary already earns part of the score
Earth-observation foundation models such as AlphaEarth and Tessera compress each 10-metre pixel into a dense vector whose individual dimensions often mix several land-surface concepts. Sparse autoencoders expand that vector over an overcomplete dictionary and keep only a few atoms active, yielding sparse, approximately monosemantic features. At matched sparsity, three sparse-autoencoder variants reconstruct better than PCA, ICA, NMF and raw dimensions; yet a control whose decoder is a frozen random dictionary also shows more specific features and finer spatial scales, so those two properties follow from sparsity itself, and only a learned dictionary cuts AlphaEarth's unexplained variance eightfold.
Earth-observation foundation models such as AlphaEarth and Tessera compress each 10-metre pixel into a dense vector whose individual dimensions often mix several land-surface concepts. Sparse autoencoders expand that vector over an overcomplete dictionary and keep only a few atoms active, yielding sparse, approximately monosemantic features. At matched sparsity, three sparse-autoencoder variants reconstruct better than PCA, ICA, NMF and raw dimensions; yet a control whose decoder is a frozen random dictionary also shows more specific features and finer spatial scales, so those two properties follow from sparsity itself, and only a learned dictionary cuts AlphaEarth's unexplained variance eightfold.
Vitus Benson（First author）
Sparse Autoencoder Earth Observation Geospatial Foundation Model Out-of-Distribution Detection
arXiv Oct 14, 2026
Remote sensing models stop being hand-built: given a harness and a sealed scorer, an agent beats published results on four benchmarks
Remote sensing models have long been built by hand, with experts designing a training recipe for each new task and tuning it against a benchmark. RSI4EO casts a task as two parts: a harness that fixes the data, pretrained weights and compute an agent may use, and a sealed verifier that returns only a score. A general-purpose coding agent then writes the training code, trains models and improves them recursively. On four benchmarks with published results, spanning optical, SAR and multispectral time-series imagery, the agent-built models surpass the state of the art, and with about 1% of the training labels they keep most of that accuracy.
Remote sensing models have long been built by hand, with experts designing a training recipe for each new task and tuning it against a benchmark. RSI4EO casts a task as two parts: a harness that fixes the data, pretrained weights and compute an agent may use, and a sealed verifier that returns only a score. A general-purpose coding agent then writes the training code, trains models and improves them recursively. On four benchmarks with published results, spanning optical, SAR and multispectral time-series imagery, the agent-built models surpass the state of the art, and with about 1% of the training labels they keep most of that accuracy.
Sijie Zhao（First author）
RSI4EO Recursive Self-Improvement Earth Observation Coding agent

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